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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

By : Greeneltch
5 (10)
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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

5 (10)
By: Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)
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Launching the Spyder IDE

Spyder can be started by passing spyder into the Anaconda prompt as follows:

(base) $ spyder

As mentioned earlier, Spyder uses the IPython interactive console. So, you can pass code line-by-line directly into the console. See the following screenshot for two lines of Python code passed one at a time:

Alternatively, you can edit a script in the editor and execute by pressing the green play button at the top of the IDE. This causes the script to be dumped into the IPython console, then run line-by-line:

The interactive IPython console also can display images and plots inline in the same console window. This is yet another feature that is very convenient for interactive data mining and rapid prototyping of analytics models:

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